An Optimal Real-Time Distributed Algorithm for Utility Maximization of Mobile Ad Hoc Cloud
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, we investigate utility maximization of mobile ad hoc cloud with an incentive mechanism to encourage mobile devices to share their idle resources. Considering that at different time slots the amount of resources demanded by the resource buyer (RB) is different and the revenue of per unit resource obtained by resource providers (RPs) is different, a real-time distributed algorithm is developed. First, by analyzing the preferences of the RB and RPs, the utility function and cost function are developed for them, respectively. Then, we propose a real-time distributed algorithm to find the maximum utility of the overall system under the price incentive mechanism, where the obtained optimal pricing can align the individual optimality with the overall system optimality. Simulation results confirm that the proposed algorithm can maximize the utility of the overall system compared with the state-of-the-art schemes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it